Group 1: Report Industry Investment Rating - No relevant content provided Group 2: Core Views of the Report - From November 3 to November 7, 2025, the REITs weighted index fell 0.49% to 97.40 points. The performance of major asset classes from high to low was convertible bonds > crude oil > stocks > pure bonds > REITs > gold. Property - type REITs fell 0.75% and franchise - type REITs rose 0.11%. [2] - As of November 7, 2025, there were 9 REITs products still in the exchange acceptance stage and 3 in the approved - to - be - listed state. [4] Group 3: Summary by Directory Secondary Market Price - Volume Performance - **Overall Secondary Market Performance**: The REITs weighted index fell 0.49% this week. Property - type REITs fell 0.75% to 110.46, and franchise - type REITs rose 0.11% to 82.68. Different industry - type REITs had varied performances. [2] - **Property - Type REITs**: The top five in terms of increase were Huaxia JINMAO Commercial REIT (4.42%), Guotai Haitong Dongjiu New Economy REIT (1.83%), Huitianfu Shanghai Real Estate Rental Housing REIT (1.81%), Huaxia First - Outlets REIT (1.52%), and Huitianfu Jiuzhoutong Pharmaceutical REIT (1.17%). High - turnover ones included Huaxia Zhonghai Commercial REIT (21.52%), etc. [2][11] - **Franchise - Type REITs**: The top five in terms of increase were Zheshang Shanghai - Hangzhou - Ningbo REIT (2.37%), Guotai Haitong Jinan Energy Heating REIT (2.25%), China Merchants Highway REIT (2.03%), Huaxia Huadian Clean Energy REIT (1.53%), and Huaxia China Communications Construction REIT (1.27%). High - turnover ones included Huaxia TBEA New Energy REIT (5.59%), etc. [2][13] - **Block Trading**: There were 5 block trading days this week, with the highest turnover on Monday at 72.2788 million yuan. The top five in terms of block trading turnover were Southern Runze Technology Data Center REIT, etc., with average discount/premium rates of 0.41%, etc. [20] Secondary Market Valuation Situation - **Property - Type REITs**: As of Friday, the top three in terms of internal rate of return (IRR) were E Fund Guangzhou Open Industrial Park REIT (8.38%), CICC Hubei KT Guanggong REIT (8.04%), and Huaxia Hefei High - tech REIT (7.33%). REITs with low P/NAV quantiles were E Fund Guangzhou Open Industrial Park REIT, etc. [3][25] - **Franchise - Type REITs**: The top three in terms of IRR were Huaxia China Communications Construction REIT (9.75%), Ping An Guangzhou Guanghe REIT (9.31%), and CICC Anhui Expressway REIT (7.60%). REITs with low P/NAV quantiles were Huaxia Yuexiu REIT, etc. [3][28] Market Correlation Statistics - The correlation coefficient between REITs and the Shanghai Composite Index was the highest at 0.20 this week. Different types of REITs had different correlation coefficients with major asset classes. [29][30] Primary Market Tracking - As of November 7, 2025, there were 9 REITs in the exchange acceptance stage and 3 in the approved - to - be - listed state, including Huaxia Anbo Warehouse Logistics REIT, etc. [4][33]

- The report tracks high-frequency stock selection factors, including Price Range Factor, Price-Volume Divergence Factor, Regret Avoidance Factor, and Slope Convexity Factor, with their respective excess returns detailed for different periods [2][3][13] - Price Range Factor measures the activity level of stocks in different intraday price ranges, reflecting investor expectations for future stock trends. It shows strong predictive performance and stable results this year [3][11][17] - Price-Volume Divergence Factor evaluates the correlation between stock price and trading volume. Lower correlation indicates higher potential for future stock price increases. However, its performance has been unstable in recent years [3][22][24] - Regret Avoidance Factor examines the proportion and degree of stock rebound after being sold by investors, leveraging behavioral finance theories. It demonstrates stable excess returns out-of-sample, indicating significant influence of regret avoidance sentiment on stock price expectations [3][25][34] - Slope Convexity Factor is constructed using high-frequency order book data, analyzing the slope and convexity of order books to assess the impact of investor patience and supply-demand elasticity on expected returns. It includes High-Level Slope Factor and High-Level Convexity Factor [3][36][39] - A high-frequency "Gold" portfolio strategy was created by equally combining the three high-frequency factors, achieving an annualized excess return of 10.09% and an IR of 2.36 [3][43][46] - A combined high-frequency and fundamental factor strategy was developed, integrating high-frequency factors with fundamental factors like consensus expectations, growth, and technical factors. This strategy achieved an annualized excess return of 14.28% and an IR of 3.41 [3][47][50]

Group 1: Report Industry Investment Rating - Not provided in the given content Group 2: Core View of the Report - The science and innovation bond market recently shows characteristics of "stable primary supply, declining secondary income center, and dominated by ETF allocation power". Investment directions can focus on short - to medium - duration varieties of high - rating entities and the science and innovation field (such as communications, medicine, high - end manufacturing), and seize the allocation opportunities of industry science and innovation bonds with excess spreads [4] Group 3: Summary by Relevant Catalog 1. Primary Issuance Scale and Structure - The supply rhythm of new science and innovation bonds is stable. From November 3 to November 7, 2025, the primary market supply scale of science and innovation bonds reached 6.146 billion yuan, and the issuance rhythm tended to be stable. Due to news disturbances and stock market rebounds, the subscription sentiment of new science and innovation bonds declined marginally, but the subscription enthusiasm remained at a relatively high level because of the relatively loose capital situation [2][12] 2. Secondary Trading Activity and Pricing - **Rating and Industry Distribution**: The ratings of outstanding science and innovation bonds are highly concentrated. Bonds with an implied rating of AA+ and above account for 72.5%, and AA - rated medium - quality individual bonds account for 23%, reflecting the financing needs of some small and medium - sized science and innovation entities. The industry distribution is dominated by traditional industries, with bonds in industries such as building decoration, public utilities, and comprehensive accounting for nearly 40%. Textile and apparel, pharmaceutical biology, power equipment, and communication industries have an excess spread of over 13bp compared with the overall credit bond valuation of the industry [3][18] - **Liquidity**: Due to the reversal of bond market trading sentiment during the week, the turnover rate of each bond variety declined slightly. The weekly turnover rate of science and innovation bonds was fixed at 1.63%, and the number of transactions also decreased to 685. Among them, the 1 - 3 - year varieties were the most actively traded [3][26] - **Yield**: Investors' expectations for exchange - traded science and innovation bonds tend to be consistent, and the deviation between the yield and valuation of the variety is generally controlled within 1.5bp. Supported by the expectation of loose liquidity, policy support, and ETF fund inflows, the average yield of 1 - to 3 - year exchange - traded science and innovation bonds declined to 1.89%, and the average yield of medium - and long - term varieties from 3 to 10 years also decreased by more than 4bp compared with last week [3][31] - **Internal Price Comparison**: In the past week, the spread between the constituent bonds and non - constituent bonds of the science and innovation bond index has narrowed. As of November 7, the spread has been compressed to 11bp, indicating that the market's recognition of non - constituent bonds of the science and innovation bond index has increased [4][35]

Core Insights - The report suggests increasing allocation to large-cap value stocks while focusing on TMT (Technology, Media, and Telecommunications) and cyclical sectors [3][30] - The industry rotation model has been optimized to adapt to market conditions, incorporating high-frequency factors and enhancing the strategy's effectiveness [4][26] - The latest industry rotation model identifies non-bank financials, steel, media, non-ferrous metals, environmental protection, and telecommunications as preferred sectors [30][33] Market Review and Fund Flow Tracking - As of October 31, 2025, the total monthly trading volume of A-shares reached 36.78 trillion yuan, with a slight decrease in daily average trading volume by 10.49% compared to the previous month [12][18] - The average stock return dispersion for the past month was 2.41%, indicating a slight decline but remaining above the median level for the past six months [12][18] - The industry rotation speed has continued to expand, significantly exceeding the average level since 2015 [12][18] Industry Rotation Model and ETF Fund Configuration - The report emphasizes the importance of focusing on large-cap value and cyclical sectors, particularly in the context of the current unclear market leadership [3][30] - The recommended ETF portfolio includes six funds: E Fund CSI 300 Non-Bank ETF, Guotai Junan CSI Steel ETF, GF CSI Media ETF, Southern CSI Non-Ferrous Metals ETF, Southern Yangtze River Protection Theme ETF, and Guotai Junan CSI All-Share Communication Equipment ETF [3][34] - The model's historical performance has shown consistent positive excess returns, outperforming major benchmark indices [5][42] Historical Performance and Model Effectiveness - The industry rotation model has maintained a strong performance over the years, achieving excess returns compared to industry averages, with a notable performance in 2025 [5][42] - The model's win rates over the past 1, 3, and 5 years are 83.33%, 69.44%, and 71.67% respectively, indicating its robustness [43][44] - The report highlights the significance of emotional and price-volume factors in capturing market dynamics, especially in weak market conditions [42][43]

1. Report Industry Investment Rating No relevant content provided. 2. Core Viewpoints of the Report - The economic growth shows that the destocking of steel is slower than the seasonal norm, with production - side开工率普遍回升 and demand - side facing various situations such as slow steel destocking and uneven performance in different sectors. - Inflation is characterized by a weak rebound in pig prices at the bottom, along with different price trends in CPI and PPI components [1][2]. 3. Summary According to the Directory 3.1 Economic Growth: Steel Destocking Slower than Seasonal Norm 3.1.1 Production: General Increase in Operating Rates - **Power Plant Daily Consumption Seasonal Rebound**: On November 10, the average daily consumption of 6 major power - generating groups was 796,000 tons, up 4.3% from November 3. On November 6, the daily consumption of power plants in eight southern provinces was 1.879 million tons, up 4.1% from October 30, driven by winter heating and industrial electricity load recovery [4][12]. - **Blast Furnace Operating Rate Recovered to Pre - Restriction Level**: On November 7, the national blast furnace operating rate was 83.2%, up 1.4 percentage points from October 31, and the capacity utilization rate was 87.8%, down 0.8 percentage points. The blast furnace operating rate of Tangshan steel mills was 91.9%, up 23.5 percentage points from October 31. However, the subsequent maintenance and production - cut efforts may increase due to weak downstream markets [4][16]. - **Tire Operating Rate Moderately Rebounded**: On November 6, the operating rate of all - steel truck tires was 65.5%, up 0.1 percentage points from October 30, and that of semi - steel car tires was 73.7%, up 0.3 percentage points. The operating rate of looms in the Jiangsu and Zhejiang regions continued to be strong [4][19]. 3.1.2 Demand: Steel Destocking Slower than Seasonal Norm - **Improvement in New Home Sales in 30 Cities on a Month - on - Month Basis**: From November 1 - 11, the average daily sales area of commercial housing in 30 large and medium - sized cities was 197,000 square meters, up 65.7% from October, but down compared with the same periods in previous years. Sales in first - tier, second - tier, and third - tier cities all declined year - on - year [4][24]. - **Weak Start in the Automobile Retail Market**: In November, retail sales were down 19% year - on - year, and wholesale sales were down 22% year - on - year. The high base last year and tightened subsidy policies contributed to the low growth [4][28]. - **Weak Fluctuation in Steel Prices**: On November 11, compared with November 4, rebar, wire rod, hot - rolled coil, and cold - rolled prices changed by + 0.3%, + 1.4%, - 0.9%, and - 0.4% respectively. Steel destocking was slower than the seasonal norm, with the inventory of five major steel products at 1.075 million tons on November 7, down 2,100 tons from October 31 [4][33]. - **Regional Differentiation in Cement Prices**: On November 11, the national cement price index rose 0.1% from November 4. The prices in the East China and Yangtze River regions showed different trends. The year - on - year decline in cement prices widened [4][34]. - **Weak Decline in Glass Prices**: On November 11, the active glass futures contract price was 1,062 yuan/ton, down 3.7% from November 4. The year - on - year and month - on - month declines in glass prices were significant [4][39]. - **End of Four - Consecutive - Increase and Turn to Decline in Container Shipping Freight Index**: On November 7, the CCFI index rose 3.6% from October 31, while the SCFI index fell 3.6%. The container shipping market is in the traditional off - season, but there may be a replenishment wave in late November and December [4][41]. 3.2 Inflation: Weak Rebound in Pig Prices at the Bottom 3.2.1 CPI: Weak Rebound in Pig Prices at the Bottom - **Weak Rebound in Pig Prices at the Bottom**: On November 11, the average wholesale price of pork was 18.1 yuan/kg, up 0.5% from November 4. Although the supply pressure will be gradually released, the overall consumption environment is still weak [4][47]. - **Moderate Increase in Agricultural Product Price Index**: On November 11, the agricultural product wholesale price index rose 0.5% from November 4. Different agricultural products showed different price trends, with chicken having the highest increase [4][52]. 3.2.2 PPI: Oil Price Rebound after Decline - **Oil Price Rebound after Decline**: On November 11, the spot prices of Brent and WTI crude oil were $64.4 and $61.0 per barrel respectively, with Brent down 1.7% and WTI up 0.8% from November 4. Supply - side and demand - side factors jointly affect the oil price [4][55]. - **Moderate Increase in Copper and Aluminum Prices**: On November 11, the prices of LME 3 - month copper and aluminum rose 1.7% and 0.2% respectively from November 4. The domestic commodity index's month - on - month decline narrowed [4][59]. - **Mixed Month - on - Month Price Changes in Industrial Products**: Since November, industrial product prices have shown different trends, with some rising and some falling. Most of the year - on - year declines in industrial product prices have converged, except for cement and glass [4][61].

Investment Rating - The report maintains a "Buy" rating for the retail industry [1] Core Insights - The retail transformation presents significant opportunities, making it one of the most aggressive sectors in China's consumption industry, potentially leading to a wave of long-term bullish companies [1][12] - The Japanese supermarket industry has experienced stable market size due to aging population and population decline, impacting consumer preferences and product categories [1][16] - Life Supermarket's success is attributed to its focus on food products, particularly processed foods, which are resilient in an aging society [2][4] Summary by Sections Japanese Supermarket Demand Changes - Population aging and income changes are the two core factors influencing supermarket demand [16] - Japan's aging rate increased from 17.6% in 1990 to 35.7% in 2022, leading to a shift in consumer demographics [16][20] - The total population peaked in 2010 and has been in decline since, suppressing overall supermarket market growth [16][20] Japanese Supermarket Supply Changes - Despite stable market size, the supply side of Japanese supermarkets has shown aggressive growth, with an increase in the number of smaller supermarkets [1][4] - Traditional large-scale supermarkets are decreasing, while food-focused supermarkets are on the rise [1][4] Life Supermarket's Rise - Life Supermarket focuses on food, with processed foods making up 88.74% of its offerings, allowing it to withstand sales declines in traditional categories [2][4] - The supermarket's location strategy involves dense store openings in populous areas, particularly in regions with positive population growth [2][4] - Revenue growth is primarily driven by new store openings rather than same-store sales, with store numbers increasing from 150 in the late 1990s to 314 by 2024 [3][4] Investment Recommendations and Insights - The aging population is a key driver for the growth of food supermarkets in China, with a high certainty of their rise [4] - The competitive landscape differs between Japan and China, with China's developed online market intensifying competition in high-density, high-income areas, while lower-tier markets remain fertile ground for offline supermarkets [4]

Group 1: Economic Downturn - The U.S. economy is currently experiencing a negative feedback loop characterized by declining income, shrinking consumption, and weak employment[2] - Since the beginning of the year, a noticeable cyclical downturn has emerged, with key indicators such as employment, consumption, and services showing continuous decline[3] - The consumer confidence index has dropped to its lowest level since June 2022, with the Michigan consumer sentiment index at 50.3[27] Group 2: Government Shutdown Impact - The ongoing U.S. government shutdown has lasted 43 days, surpassing the previous record of 35 days in December 2018[28] - The shutdown has led to approximately $24 billion in federal spending being paused, with the Congressional Budget Office estimating a 2% decline in U.S. economic growth for Q4[4] - The shutdown has also caused liquidity tightening in financial markets, contributing to a significant drop in risk assets such as gold, Bitcoin, and U.S. stocks[32] Group 3: Structural Distortions from AI Investment - There is a clear "K-shaped" divergence in U.S. exports, with AI-related sectors performing exceptionally well while traditional consumer goods exports continue to weaken[36] - AI investments have led to a surge in demand for semiconductors and related infrastructure, with Taiwan's exports to the U.S. increasing by 144.3% in October[36] - The reliance on AI has created a structural dependency that may increase long-term financial system vulnerabilities, as any fluctuations in AI could trigger broader economic disruptions[45]

Economic Impact of AI - AI-related investments contributed 1.57 percentage points to the US GDP growth in the first half of 2025, surpassing the contribution from private consumption at 1.06 percentage points[6] - In Q1 2025, AI investments boosted GDP growth by 1.3 percentage points, exceeding the peak contribution during the dot-com bubble (1.16 percentage points in Q2 1999)[6] - The nominal value added from data processing services increased to 1.75% of GDP, up from an average of 1.04% from 2013-2019, while manufacturing's share fell to 9.98%, marking a significant decline[12] AI and Employment - The penetration rate of AI technology in the workforce remains low, with only 6 out of 20 major industries exceeding a 10% usage rate, the highest being the IT sector at approximately 25%[43] - Job losses attributed to AI are overstated; the primary reasons for layoffs are related to macroeconomic factors rather than direct AI impacts[48] - AI's influence on hiring plans is evident, with companies likely to hire fewer employees in the future, but current layoffs are more linked to economic cycles[43] Financial Sector Vulnerabilities - In 2025, the total bond issuance by major tech firms reached $103.8 billion, indicating a growing reliance on external financing amid concerns over the sustainability of AI investments[78] - The private credit market has seen significant growth, with total assets under management rising from approximately $100 billion in 2010 to nearly $2.2 trillion by 2024[80] - The increasing dependence on private credit raises concerns about transparency and risk, particularly as tech firms face pressures to demonstrate profitability[79]

Investment Rating - The report maintains a "Buy" rating for the company [3] Core Insights - The company has adjusted its revenue forecasts for 2025 to 2027 to be 31.7 billion, 47.3 billion, and 67.1 billion RMB respectively, with net profits of 4.0 billion, 7.5 billion, and 12.5 billion RMB, and EPS of 3.05, 5.75, and 9.56 RMB [3] - The company reported a revenue of 7.95 billion RMB in Q3, exceeding expectations with a year-over-year growth of 47.5% [9] - The delivery volume of LiDAR units in Q3 reached 441,000, a year-over-year increase of 228.9%, with ADAS LiDAR deliveries at 381,000, up 193.1% [9] - The gross margin for Q3 was 42.1%, maintaining a high level, and net profit significantly exceeded expectations, with GAAP earnings of 2.56 billion RMB [9] - The company has raised its full-year GAAP net profit guidance to 3.5-4.5 billion RMB [9] Summary by Sections Financial Performance - Revenue for 2023 is projected at 2,077.16 million RMB, with a growth rate of 10.66% [8] - The company expects to achieve a net profit of 400.41 million RMB in 2025, with a growth rate of 491.11% [8] - The diluted EPS is forecasted to be 3.05 RMB in 2025 [8] Operational Metrics - Operating expenses (OpEx) for Q3 were 316 million RMB, fully covered by gross profit, showing a year-over-year improvement of 7.9% [9] - The company has secured partnerships with major ADAS clients for 2026 model year vehicles, ensuring a strong customer base [9] Market Position - The company has successfully established a partnership with a leading Chinese EV manufacturer for its ETX product line, indicating strong market validation [9]

Investment Rating - The report assigns a "Buy" rating to the company with a target price of 32.88 RMB based on a 12x valuation for 2025 [6]. Core Views - The company has a significant cost advantage in oil and gas production, leading to excellent profitability. The average production cost is projected to be 29.56 USD/barrel in 2024, lower than its peers [3]. - The company's capital expenditure (CAPEX) remains high, supporting stable growth in reserves and production. The CAPEX is expected to reach 18.08 billion USD in 2024, nearly double that of ConocoPhillips [4]. - The company has a valuation advantage compared to international oil and gas companies, with its PV-10 valuation significantly lower than most peers [5]. Summary by Sections 1. Cost Advantages in Oil Production - The company has demonstrated a notable reduction in production costs over recent years, with a projected average production cost of 29.56 USD/barrel in 2024, the lowest among China's "Big Three" oil companies [3][17]. - The primary source of cost advantage is operational costs, which have decreased from 10.44 USD/barrel in 2012 to 7.61 USD/barrel in 2024 [26]. 2. Production Structure and CAPEX - The company has shown rapid and stable growth in oil and gas production, with a projected increase from 889 thousand barrels/day in 2012 to 1930 thousand barrels/day in 2024 [36]. - The CAPEX level is industry-leading, with a projected 18.08 billion USD in 2024, significantly higher than its peers [4][61]. - High CAPEX levels contribute to resource reserves and lifespan advantages, supporting long-term production growth [63]. 3. Valuation Advantages - The report anticipates a continued oversupply in the international oil market, with Brent crude prices expected to fluctuate downwards [68]. - The company's valuation metrics, such as PE and EV/EBITDA, are approximately 20%-50% lower than major international oil companies, indicating a valuation advantage [5].